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Elicitability and backtesting: Perspectives for banking regulation [PDF]
, 2017 Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations.
Nolde, Natalia, Ziegel, Johanna F.
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ANALYSIS OF THE INVESTMENT ARBITRAGE STRATEGY USING FINANCIAL MULTIPLIERS
Статистика и экономика, 2016 This article describes an algorithm for stock pairs trading using financial multipliers of underlying companies. This algorithm has been tested on historical data and compared with classical Bollinger bands strategy.
Dmitry S. Pashkov
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Ekonomika, 2016 Within this paper we shall research the validation methods of the risk model and we shall provide an overview of the existing literature which deals with validation and performance assessment of the VaR (Value at Risk) model. The importance of backtesting of the risk model stems from the fact that credit institutions have been allowed by regulatory ...
Ivica Terzic, Marko Milojevic
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Markov-Switching GARCH Modelling of Value-at-RisK [PDF]
, 2008 This paper proposes an asymmetric Markov regime-switching (MS) GARCH model to estimate value-at-risk (VaR) for both long and short positions. This model improves on existing VaR methods by taking into account both regime change and skewness or leverage ...
Coakley, J, Nankervis, JC, Sajjad, R
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Journal of Information Systems Engineering and Business Intelligence, 2021 Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other,
Brina Miftahurrohmah +2 more
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Correctness of backtest engines [PDF]
The Journal of Investment Strategies, 2017 In recent years several trading platforms appeared which provide a backtest engine to calculate historic performance of self designed trading strategies on underlying candle data. The construction of a correct working backtest engine is, however, a subtle task as shown by Maier-Paape and Platen (cf. arXiv:1412.5558 [q-fin.TR]).
Robert L\\\"ow +2 more
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Barekeng, 2023 The Value at Risk (VaR) method refers to a statistical risk measurement tool used to determine the maximum loss of an investment, while the distribution that must be met is the normal distribution.
Alimatun Najiha +2 more
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, 2000 With the implementation of Value-at-Risk (VaR) models a new chapter of risk management was opened. Their ultimate goal is to quantify the uncertainty about the amount that may be lost or gained on a portfolio over a given period of time. Most generally, the uncertainty is expressed by a forecast distribution P t+1 for period t+1 associated with the ...
Härdle, Wolfgang, Stahl, Gerhard
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Forecasting Value-at-Risk under Different Distributional Assumptions
Econometrics, 2016 Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR).
Manuela Braione, Nicolas K. Scholtes
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Sensitivity Analysis of Two-Step Multinomial Backtests for Evaluating Value-at-Risk [PDF]
تحقیقات مالی, 2022 Objective: Nowadays, the measurement of the risk of the marketplace has a significant effect on investments; however, the inadequate evaluation of this risk will cause a financial crisis and possible bankruptcy.
Mohamad Ali Rastegar, Mehdi Hemati
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